We use the last RNN state as the sentence representation: \({\bf g} = {\bf r}_L\) .
As seen, the RNN-based aggregation repeatedly revises the sentence representations of the sequence with each recurrent step. As a side effect coming together with the proposed approach, the added recurrent inductive bias of RNNs has proven beneficial for many sequence-to-sequence learning tasks such as machine translation [1]}.
